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Objective: Some atypical antipsychotics are associated with metabolic side effects, which are risk factors for gestational diabetes. The authors examined the risk of developing gestational diabetes associated with the continuation of treatment with aripiprazole, ziprasidone, quetiapine, risperidone, and olanzapine during pregnancy compared with discontinuation of these antipsychotic drugs.Method: Nondiabetic pregnant women who were linked to a live-born infant and enrolled in Medicaid (2000-2010) and who received one or more prescriptions dispensed for an antipsychotic drug during the 3 months before pregnancy were included in the analyses. Among 1,543,334 pregnancies, some expectant mothers at baseline were receiving treatment with aripiprazole (N=1,924), ziprasidone (N=673), quetiapine (N=4,533), risperidone (N=1,824), or olanzapine (N=1,425). For each antipsychotic drug, women with two or more dispensings ("continuers") were compared with women with no dispensings ("discontinuers") during the first half of pregnancy. A generalized linear model and propensity-score stratification were used to obtain absolute and relative risks of developing gestational diabetes, with adjustment for confounders.Results: Women who continued antipsychotic treatment during pregnancy generally had higher comorbidity and longer baseline antipsychotic use. The crude risk of developing gestational diabetes among continuers compared with discontinuers, respectively, was 4.8% and 4.5% for aripiprazole, 4.2% and 3.8% for ziprasidone, 7.1% and 4.1% for quetiapine, 6.4% and 4.1% for risperidone, and 12.0% and 4.7% for olanzapine. The adjusted relative risks were 0.82 (95% CI=0.50-1.33) for aripiprazole, 0.76 (95% CI=0.29-2.00) for ziprasidone, 1.28 (95% CI=1.01-1.62) for quetiapine, 1.09 (95% CI=0.70-1.70) for risperidone, and 1.61 (95% CI=1.13-2.29) for olanzapine.Conclusions: Compared with women who discontinued use of an atypical antipsychotic medication before the start of pregnancy, women who continued treatment with olanzapine or quetiapine had an increased risk of gestational diabetes that may be explained by the metabolic effects associated with these two drugs. [ABSTRACT FROM AUTHOR]

Background: Influenza causes substantial morbidity, particularly among older individuals. Updated data on the effectiveness of currently licensed vaccines in this population are needed., Methods: At Kaiser Permanente Southern California, we conducted a retrospective cohort study to evaluate comparative vaccine effectiveness (cVE) of high-dose (HD), adjuvanted, and standard-dose (SD) cell-based influenza vaccines, relative to the SD egg-based vaccine. We included adults aged ≥65 years who received an influenza vaccine between 1 August 2022 and 31 December 2022, with follow-up up to 20 May 2023. Primary outcomes were: (1) influenza-related medical encounters and (2) polymerase chain reaction (PCR)-confirmed influenza-related hospitalization. Adjusted hazard ratios (aHR) were estimated by Cox proportional hazards regression, adjusting for confounders using inverse probability of treatment weighting (IPTW). cVE (%) was calculated as (1-aHR) × 100 when aHR ≤1, and ([1/aHR]-1) × 100 when aHR >1., Results: Our study population (n = 495 119) was 54.9% female, 46.3% non-Hispanic White, with a median age of 73 years (interquartile range [IQR] 69-79). Characteristics of all groups were well balanced after IPTW. Adjusted cVEs against influenza-related medical encounters in the HD, adjuvanted, and SD cell-based vaccine groups were 9.1% (95% confidence interval [CI]: .9, 16.7), 16.9% (95% CI: 1.7, 29.8), and -6.3 (95% CI: -18.3, 6.9), respectively. Adjusted cVEs against PCR-confirmed hospitalization in the HD, adjuvanted, and SD cell-based groups were 25.1% (95% CI: .2, 43.8), 61.6% (95% CI: 18.1, 82.0), and 26.4% (95% CI: -18.3, 55.7), respectively., Conclusions: Compared to the SD egg-based vaccine, HD and adjuvanted vaccines conferred additional protection against influenza-related outcomes in the 2022-2023 season in adults ≥65 years. Our results provide real-world evidence of the comparative effectiveness of currently licensed vaccines., Competing Interests: Potential conflicts of interests. J. H. K., E. R., L. S. S., L. Q., B. K. A., Y. L., J. E. T., G. S. L., P. P. M., and H. F. T. are employees of Kaiser Permanente Southern California, which has been contracted by Moderna, Inc. to conduct this study. Y. P., T. S., and E. J. A., are employees of and shareholders in Moderna, Inc. J. H. K. received funding from GlaxoSmithKline and Moderna unrelated to this manuscript. E. R. received funding from GlaxoSmithKline unrelated to this manuscript. L. S. S. received funding from GlaxoSmithKline, Dynavax, and Moderna unrelated to this manuscript. L. Q. received funding from GlaxoSmithKline, Dynavax, and Moderna unrelated to this manuscript. B. K. A. received funding from GlaxoSmithKline, Dynavax, Genentech, and Moderna unrelated to this manuscript. Y. L. received funding from GlaxoSmithKline, Pfizer, and Moderna unrelated to this manuscript. J. E. T. received funding from GlaxoSmithKline and Moderna unrelated to this manuscript. G. S. L. received funding from GlaxoSmithKline and Moderna unrelated to this manuscript. P. P. M. received funding from GlaxoSmithKline unrelated to this manuscript. H. F. T. received funding from GlaxoSmithKline and Moderna unrelated to this manuscript; H. F. T. also served on advisory boards for Janssen and Pfizer. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed., (© The Author(s) 2024. Published by Oxford University Press on behalf of Infectious Diseases Society of America.)

Objective: To compare healthcare resource utilization (HCRU) and all-cause medical costs among individuals aged ≥50 years who received influenza and COVID-19 vaccines on the same day and those who received influenza vaccine only., Methods: We conducted a retrospective cohort study leveraging Optum's de-identified Clinformatics DataMart from 8/31/2021 to 7/31/2023. Individuals aged ≥50 years continuously enrolled in health plans for 1 year prior and until 7/31/2023 were included. Two cohorts were formed based on vaccination status between 8/31/2022 and 1/31/2023: co-administered influenza and COVID-19 vaccines (co-admin cohort) and influenza vaccine only (influenza cohort). Associations between vaccination status and all-cause, influenza-related, COVID-related, pneumonia-related, and cardiorespiratory-related hospitalization, outpatient or emergency room visits and all-cause medical costs were estimated by weighted generalized linear models, adjusting for confounding by stabilized inverse probability of treatment weighting., Results: 613,156 (mean age: 71) and 1,340,011 (mean age: 72) individuals were included in the co-admin and influenza cohorts, respectively. After weighting, the baseline characteristics were balanced between cohorts. The co-admin cohort was at statistically significant lower risk of all-cause (RR: 0.95, 95% CI: 0.93-0.96), COVID-19-related (RR: 0.59, 95% CI: 0.56-0.63), cardiorespiratory-related (RR: 0.94, 95% CI: 0.93-0.96) and pneumonia-related (RR: 0.86, 95% CI: 0.83-0.90) hospitalization but not influenza-related hospitalizations (RR: 0.91, 95% CI: 0.81, 1.04) compared with the influenza cohort. Co-administration was associated with 3% lower all-cause medical cost (cost ratio: 0.974, 95% CI: 0.968, 0.979) during the follow-up period compared to receiving influenza vaccine only., Limitations: Limitations include the potential residual confounding bias in observational data, measurement errors from claims data, and that the cohort was followed for a single season., Conclusion: Receiving co-administered COVID-19 and influenza vaccines versus only receiving influenza vaccination reduced the risk of HCRU, especially COVID-19-related hospitalization and all-cause medical costs. Increasing vaccine coverage, particularly for COVID-19, might have public health and economic benefits.

Understanding the complexity of care delivery and care coordination for patients with multiple chronic conditions is challenging. Network analysis can model the relationship between providers and patients to find factors associated with patient mortality. We constructed a network by connecting the providers through shared patients, which was then partitioned into tightly connected communities using a community detection algorithm. After adjusting for patient characteristics, the odds ratio of death for one standard deviation increase in degree centrality ratio between primary care providers (PCPs) and non-PCPs was 0.95 (0.92-0.98). Our result suggest that the centrality of PCPs may be a modifiable factor for improving care delivery. We demonstrated that network analysis can be used to find higher order features associated with health outcomes in addition to patient-level features., (©2022 AMIA - All rights reserved.)

Social Determinants of Health (SDoH) are an increasingly important part of the broader research and public health efforts in understanding individuals' physical and mental well-being. Despite this, non-clinical factors affecting health are poorly recorded in electronic health databases and techniques to study how SDoH might relate to population outcomes are lacking. This paper proposes an approach to systematically identify and quantify associations between SDoH and health-related outcomes in a specific cohort of people by (1) leveraging published evidence from literature to build a knowledge graph of health and social factor associations and (2) analysing a large dataset of claims and medical records where those associations may be found. This work demonstrates how the proposed approach could be used to generate hypotheses and inform further research on SDoH in a data-driven manner., (©2021 AMIA - All rights reserved.)

Deep learning architectures have an extremely high-capacity for modeling complex data in a wide variety of domains. However, these architectures have been limited in their ability to support complex prediction problems using insurance claims data, such as readmission at 30 days, mainly due to data sparsity issue. Consequently, classical machine learning methods, especially those that embed domain knowledge in handcrafted features, are often on par with, and sometimes outperform, deep learning approaches. In this paper, we illustrate how the potential of deep learning can be achieved by blending domain knowledge within deep learning architectures to predict adverse events at hospital discharge, including readmissions. More specifically, we introduce a learning architecture that fuses a representation of patient data computed by a self-attention based recurrent neural network, with clinically relevant features. We conduct extensive experiments on a large claims dataset and show that the blended method outperforms the standard machine learning approaches., (©2021 AMIA - All rights reserved.)

Using electronic health data to predict adverse drug reaction (ADR) incurs practical challenges, such as lack of adequate data from any single site for rare ADR detection, resource constraints on integrating data from multiple sources, and privacy concerns with creating a centralized database from person-specific, sensitive data. We introduce a federated learning framework that can learn a global ADR prediction model from distributed health data held locally at different sites. We propose two novel methods of local model aggregation to improve the predictive capability of the global model. Through comprehensive experimental evaluation using real-world health data from 1 million patients, we demonstrate the effectiveness of our proposed approach in achieving comparable performance to centralized learning and outperforming localized learning models for two types of ADRs. We also demonstrate that, for varying data distributions, our aggregation methods outperform state-of-the-art techniques, in terms of precision, recall, and accuracy., (©2019 AMIA - All rights reserved.)